Merge branch 'pr/3591' into master.

See numpy#3591

numpy/lib/twodim_base.py

@@ -585,51 +585,60 @@ def histogram2d(x, y, bins=10, range=None, normed=False, weights=None):
 
     Examples
     --------
-    2D-histogram with variable bin width:
-
-    import matplotlib as ml
-    import matplotlib.pyplot as plt
-    import numpy as np
-
-    # First we define the bin edges                                                
-    xedges = [0, 1, 1.5, 3, 5]
-    yedges = [0, 2, 3, 4, 6]
-
-    # Next we create a histogram H with random bin content                         
-    x = np.random.normal(3, 1, 100)
-    y = np.random.normal(1, 1, 100)
-    H, xedges, yedges = np.histogram2d(y, x, bins=(xedges, yedges))
-
-    # Or we fill the histogram H with a determined bin content                     
-    H = np.ones((4, 4)).cumsum().reshape(4, 4)
-    print H[::-1]  # This shows the bin content in the order as plotted            
-
-    fig = plt.figure(figsize=(7, 3))
-    # Imshow can only do an equidistant representation of bins                     
-    ax = fig.add_subplot(131)
-    ax.set_title('imshow:\nequidistant')
-    im = plt.imshow(H, interpolation='nearest', origin='low',
-                    extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
-
-    # pcolormesh can displaying exact bin edges                                    
-    ax = fig.add_subplot(132)
-    ax.set_title('pcolormesh:\nexact bin edges')
-    X, Y = np.meshgrid(xedges, yedges)
-    ax.pcolormesh(X, Y, H)
-    ax.set_aspect('equal')
-    
-    # NonUniformImage displays exact bin edges with interpolation                  
-    ax = fig.add_subplot(133)
-    ax.set_title('NonUniformImage:\ninterpolated')
-    im = ml.image.NonUniformImage(ax, interpolation='bilinear')
-    xcenters = xedges[:-1] + 0.5 * (xedges[1:] - xedges[:-1])
-    ycenters = yedges[:-1] + 0.5 * (yedges[1:] - yedges[:-1])
-    im.set_data(xcenters, ycenters, H)
-    ax.images.append(im)
-    ax.set_xlim(xedges[0], xedges[-1])
-    ax.set_ylim(yedges[0], yedges[-1])
-    ax.set_aspect('equal')
-    plt.show()
+    >>> import matplotlib as mpl
+    >>> import matplotlib.pyplot as plt
+
+    Construct a 2D-histogram with variable bin width. First define the bin
+    edges:
+
+    >>> xedges = [0, 1, 1.5, 3, 5]
+    >>> yedges = [0, 2, 3, 4, 6]
+
+    Next we create a histogram H with random bin content:
+
+    >>> x = np.random.normal(3, 1, 100)
+    >>> y = np.random.normal(1, 1, 100)
+    >>> H, xedges, yedges = np.histogram2d(y, x, bins=(xedges, yedges))
+
+    Or we fill the histogram H with a determined bin content:
+
+    >>> H = np.ones((4, 4)).cumsum().reshape(4, 4)
+    >>> print H[::-1]  # This shows the bin content in the order as plotted
+    [[ 13.  14.  15.  16.]
+     [  9.  10.  11.  12.]
+     [  5.   6.   7.   8.]
+     [  1.   2.   3.   4.]]
+
+    Imshow can only do an equidistant representation of bins:
+
+    >>> fig = plt.figure(figsize=(7, 3))
+    >>> ax = fig.add_subplot(131)
+    >>> ax.set_title('imshow:\nequidistant')
+    >>> im = plt.imshow(H, interpolation='nearest', origin='low',
+                        extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
+
+    pcolormesh can displaying exact bin edges:
+
+    >>> ax = fig.add_subplot(132)
+    >>> ax.set_title('pcolormesh:\nexact bin edges')
+    >>> X, Y = np.meshgrid(xedges, yedges)
+    >>> ax.pcolormesh(X, Y, H)
+    >>> ax.set_aspect('equal')
+
+    NonUniformImage displays exact bin edges with interpolation:
+
+    >>> ax = fig.add_subplot(133)
+    >>> ax.set_title('NonUniformImage:\ninterpolated')
+    >>> im = mpl.image.NonUniformImage(ax, interpolation='bilinear')
+    >>> xcenters = xedges[:-1] + 0.5 * (xedges[1:] - xedges[:-1])
+    >>> ycenters = yedges[:-1] + 0.5 * (yedges[1:] - yedges[:-1])
+    >>> im.set_data(xcenters, ycenters, H)
+    >>> ax.images.append(im)
+    >>> ax.set_xlim(xedges[0], xedges[-1])
+    >>> ax.set_ylim(yedges[0], yedges[-1])
+    >>> ax.set_aspect('equal')
+    >>> plt.show()
+
     """
     from numpy import histogramdd